Broken examples due to pandas v3.0.0 (#2664)

* .resample('m') -> .resample('ME')

Broken on pandas v3.0.0
v2.1 docs https://pandas.pydata.org/pandas-docs/version/2.1/user_guide/timeseries.html#dateoffset-objects
v3.0 docs https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects

Related to #2600

* .shift(freq="-30T") -> .shift(freq="-30min")

* freq='1T' -> freq='1min'

* freq='H' -> freq='h'

* resample('m') -> resample('ME') [2 - another file]

* shift(freq='-30T') -> shift(freq='-30min') [2 - missed entry]

* pandas.read_csv param delim_whitespace removed

Fix per https://pandas.pydata.org/pandas-docs/stable/whatsnew/v3.0.0.html (find text delim_whitespace)

Issue/discussion: pandas-dev/pandas#55569

* Update v0.14.1.rst

* Whatsnew rewording in imperative mood

As per https://common-changelog.org/

Author: echedey-ls

benchmarks/benchmarks/scaling.py

@@ -15,7 +15,7 @@ def setup(self):
         lon = np.array((4.99, 5, 5.01))
         self.coordinates = np.array([(lati, loni) for
                                      (lati, loni) in zip(lat, lon)])
-        self.times = pd.date_range('2019-01-01', freq='1T', periods=self.n)
+        self.times = pd.date_range('2019-01-01', freq='1min', periods=self.n)
         self.positions = np.array([[0, 0], [100, 0], [100, 100], [0, 100]])
         self.clearsky_index = pd.Series(np.random.rand(self.n),
                                         index=self.times)

docs/examples/adr-pvarray/plot_fit_to_matrix.py

@@ -51,7 +51,7 @@
 25        1000         75.0  273.651
 26        1100         75.0  301.013
 '''
-df = pd.read_csv(StringIO(iec61853data), delim_whitespace=True)
+df = pd.read_csv(StringIO(iec61853data), sep=r"\s+")
 
 # %%
 #

docs/examples/agrivoltaics/plot_diffuse_PAR_Spitters_relationship.py

@@ -57,7 +57,7 @@
 
 solar_position = pvlib.solarposition.get_solarposition(
     # TMY timestamp is at end of hour, so shift to center of interval
-    tmy.index.shift(freq="-30T"),
+    tmy.index.shift(freq="-30min"),
     latitude=metadata["latitude"],
     longitude=metadata["longitude"],
     altitude=metadata["altitude"],

docs/examples/bifacial/plot_bifi_model_mc.py

@@ -40,7 +40,7 @@
 # create site location and times characteristics
 lat, lon = 36.084, -79.817
 tz = 'Etc/GMT+5'
-times = pd.date_range('2021-06-21', '2021-6-22', freq='1T', tz=tz)
+times = pd.date_range('2021-06-21', '2021-6-22', freq='1min', tz=tz)
 
 # create site system characteristics
 axis_tilt = 0

docs/examples/bifacial/plot_bifi_model_pvwatts.py

@@ -32,7 +32,7 @@
 # using Greensboro, NC for this example
 lat, lon = 36.084, -79.817
 tz = 'Etc/GMT+5'
-times = pd.date_range('2021-06-21', '2021-06-22', freq='1T', tz=tz)
+times = pd.date_range('2021-06-21', '2021-06-22', freq='1min', tz=tz)
 
 # create location object and get clearsky data
 site_location = location.Location(lat, lon, tz=tz, name='Greensboro, NC')

docs/examples/bifacial/plot_pvfactors_fixed_tilt.py

@@ -30,7 +30,7 @@
 # %%
 # First, generate the usual modeling inputs:
 
-times = pd.date_range('2021-06-21', '2021-06-22', freq='1T', tz='Etc/GMT+5')
+times = pd.date_range('2021-06-21', '2021-06-22', freq='1min', tz='Etc/GMT+5')
 loc = location.Location(latitude=40, longitude=-80, tz=times.tz)
 sp = loc.get_solarposition(times)
 cs = loc.get_clearsky(times)

docs/examples/floating-pv/plot_floating_pv_cell_temperature.py

@@ -140,7 +140,7 @@
 
 solar_position = pvlib.solarposition.get_solarposition(
     # TMY timestamp is at end of hour, so shift to center of interval
-    tmy.index.shift(freq='-30T'),
+    tmy.index.shift(freq='-30min'),
     latitude=metadata['latitude'],
     longitude=metadata['longitude'],
     altitude=metadata['altitude'],

docs/examples/irradiance-decomposition/plot_diffuse_fraction.py

@@ -35,7 +35,7 @@
 # NOTE: TMY3 files timestamps indicate the end of the hour, so shift indices
 # back 30-minutes to calculate solar position at center of the interval
 solpos = get_solarposition(
-    greensboro.index.shift(freq="-30T"), latitude=metadata['latitude'],
+    greensboro.index.shift(freq="-30min"), latitude=metadata['latitude'],
     longitude=metadata['longitude'], altitude=metadata['altitude'],
     pressure=greensboro.pressure*100,  # convert from millibar to Pa
     temperature=greensboro.temp_air)

docs/examples/irradiance-transposition/plot_seasonal_tilt.py

@@ -96,6 +96,6 @@ def get_orientation(self, solar_zenith, solar_azimuth):
     'Seasonal 20/40 Production': mc.results.ac,
     'Fixed 30 Production': mc2.results.ac,
 })
-results.resample('m').sum().plot()
+results.resample('ME').sum().plot()
 plt.ylabel('Monthly Production')
 plt.show()

docs/examples/irradiance-transposition/plot_transposition_gain.py

@@ -80,7 +80,7 @@ def calculate_poa(tmy, solar_position, surface_tilt, surface_azimuth):
     column_name = f"FT-{tilt}"
     # TMYs are hourly, so we can just sum up irradiance [W/m^2] to get
     # insolation [Wh/m^2]:
-    df_monthly[column_name] = poa_irradiance.resample('m').sum()
+    df_monthly[column_name] = poa_irradiance.resample('ME').sum()
 
 # single-axis tracking:
 orientation = tracking.singleaxis(solar_position['apparent_zenith'],
@@ -95,10 +95,10 @@ def calculate_poa(tmy, solar_position, surface_tilt, surface_azimuth):
                                solar_position,
                                orientation['surface_tilt'],
                                orientation['surface_azimuth'])
-df_monthly['SAT-0.4'] = poa_irradiance.resample('m').sum()
+df_monthly['SAT-0.4'] = poa_irradiance.resample('ME').sum()
 
 # calculate the percent difference from GHI
-ghi_monthly = tmy['ghi'].resample('m').sum()
+ghi_monthly = tmy['ghi'].resample('ME').sum()
 df_monthly = 100 * (df_monthly.divide(ghi_monthly, axis=0) - 1)
 
 df_monthly.plot()